Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance

ABSTRACT

In the era of globalization, student placement is very challenging issue for all educational institutions. For engineering institutions, placement is a key factor to maintain good ranking in the university as well as in other national and international ranking agencies. In this paper, we have proposed a few supervised machine learning classifiers which may be used to predict the placement of a student in the IT industry based on their academic performance in class Tenth, Twelve, Graduation, and Backlog till date in Graduation. We also compare the results of different proposed classifiers. Various parameters used to compare and analyze the results of different developed classifiers are accuracy score, percentage accuracy score, confusion matrix, heatmap, and classification report. Classification report generated by developed classifiers consists of parameters precision, recall, f1-score, and support. The classification algorithms Support Vector Machine, Gaussian Naive Bayes, K-Nearest Neighbor, Random Forest, Decision Tree, Stochastic Gradient Descent, Logistic Regression, and Neural Network are used to develop the classifiers. All the developed classifiers are also tested on new data which are excluded from the dataset used in the experiment.

Acknowledgments

This research is accomplished by a team of three faculty members from the Department of Computer Science and Engineering at Shri Ram Murti Smarak College of Engineering and Technology (SRMSCET), Bareilly, Uttar Pradesh (India). The authors are thankful to the management of SRMSCET for motivating and providing all kinds of research infrastructure and facilities such as print and online journals of repute in the college library and computing and Internet facility in the department. The authors are also thankful to all final year students of CSE & IT branch of SRMSCET for being prompt and supportive in the data collection process. We are thankful to our family members and colleagues for their support and cooperation. Finally, our special thanks to faculty members Hiresh Gupta and Durgesh Tripathi for their valuable direction.

Web References

Accessed May 04, 2020. https://www.poornima.edu.in/role-of-university-in-students-placement/

Accessed June 25, 2020.https://www.geeksforgeeks.org/basic-concept-classification-data-mining/

Accessed June 27, 2020. https://www.coursera.org/lecture/machine-learning/welcome-RKFpn/